Exploring daily wind data using the Meteostat Python Library

Group Members: Travis, Ira, Micah

Course: Data Science – Phase 1
Goal: Explore and compare wind trends in distinct U.S. regions

Source: Meteostat Python API

Dataset Type: Aggregated weather observations per station

Key Variables
  • wspd: Average wind speed (km/h)
  • wdir: Mean wind direction (degrees)
  • tavg: Average air temperature (°C)
  • coco: Condition code

Time Period: 2024

Locations: ~30

Frame: Hourly, Daily, Monthly with a focus on Hourly

Units: Metric (km/h, degrees, °C)

Region Station (City) Station ID Climate Context
Florida Miami Intl Airport 72202 Hurricane-prone coastal region
Oklahoma Oklahoma City 72353 Tornado Alley with frequent severe winds
Pennsylvania Pittsburgh Intl Airport 72520 Inland relative known temperature
California Los Angeles Intl Airport 72295 Pacific coastal winds and mountain effects

  1. How do wind patterns change by region?

  2. What are some case studies of extreme weather?

  3. How do geograhical feature (lakes, oceans, mountains, deserts, plains) impact?